Scientist II, ML - Guided Protein Design Evaluation

Profluent • Emeryville, CA

11d • $147,000 - $180,000 • Onsite

About The Position

Profluent is an AI-first protein design company founded in 2022, focused on developing deep generative models to design and validate novel, functional proteins for revolutionizing biomedicine. The company is backed by leading investors and has raised over $150M. The ML Design Evaluation (MDE) program is expanding, and this role will be a key part of a cross-functional program that runs dedicated, structured design campaigns to generate high-quality experimental data for Profluent’s protein design models. MDE operates at the intersection of ML, Biology, and Bioinformatics, with its output directly contributing to improved models and accelerated protein design campaigns. The scientist will partner with the MDE Lead on the scientific substance of the program, owning the translation between ML team needs and experimental system capabilities. This is a hands-on technical role requiring collaboration with ML and Protein Design scientists to develop experimental designs, review assay protocols, troubleshoot data quality, and understand data usage in model training.

Requirements

• PhD in Molecular Biology, Biochemistry, Protein Engineering, Biophysics, Immunology, or a closely related field; or MS with equivalent industry experience
• 5+ years of hands-on experience with protein engineering and functional characterization, including biochemical activity, binding, stability, and/or expression assays
• Direct experience with therapeutic antibody discovery and engineering (e.g., affinity maturation, developability optimization, humanization, format engineering) and the assays that support it (binding kinetics, epitope characterization, biophysical and developability panels)
• Strong working knowledge of NGS-based screening workflows (e.g., deep mutational scanning, amplicon sequencing, high-throughput activity screens, antibody display library sequencing) and what makes that data usable for modeling
• Demonstrated ability to define assay quality standards (signal/noise, reproducibility, plate-level controls) and hold experimental workflows to them
• Fluent working across scientific disciplines; can talk protein chemistry and antibody biology with biologists and model-guided design approaches with ML scientists without losing either audience

Nice To Haves

• Prior experience closing Design–Build–Test–Learn loops in an industrial protein design, antibody engineering, or directed evolution setting
• Experience with display-based antibody discovery platforms (phage, yeast, mammalian) and/or single-cell B-cell screening workflows
• Experience scoping and overseeing externally run assays at CROs, including technical evaluation of vendor platforms
• Comfortable with Python/pandas and SQL at the level needed to inspect, QC, and reason about experimental datasets independently
• Familiarity with LIMS and project-management systems and experience defining data & metadata schemas for experimental data
• Exposure to kinases, nucleases, recombinases, or gene editing enzymes is a plus
• Track record of publishing, presenting, or shipping work at the intersection of protein engineering and machine learning

Responsibilities

• Partner with ML and Protein Design leads to scope MDE campaigns; including target selection, choice of assays, and what readouts are needed to improve the next generation of models
• Define assay readiness levels, controls, and QC acceptance criteria for each campaign, and enforce them across internal execution and external CROs
• Review incoming data (biochemical, biophysical, NGS-based screens) for scientific soundness before it is accepted into the data warehouse and used for model training
• Work with Bioinformatics on metadata schemas and data ingestion so that every dataset is consistent, queryable, and traceable back to its parent project.
• Serve as the scientific point of contact for CRO technical scoping; evaluate whether a vendor’s platform, assay conditions, and controls are fit for purpose for ML-guided optimization
• Contribute to campaign charters, benchmarking assay design, and post-campaign readouts; help turn individual experiments into a growing institutional dataset
• Represent the MDE program in cross-functional technical forums and help raise the data-quality bar across the organization

Benefits

• Competitive compensation package with equity participation
• Comprehensive benefits including health/dental/vision insurance
• Generous PTO policy and commitment to work-life balance
• Professional development opportunities in a cutting-edge field at the intersection of AI and biology

What This Job Offers

• Job Type: Full-time
• Career Level: Senior